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DCNN-based prediction model for detection of age-related macular degeneration from color fundus images. Med Biol Eng Comput 2022; 60:1431-1448. [DOI: 10.1007/s11517-022-02542-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2020] [Accepted: 02/23/2022] [Indexed: 11/25/2022]
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Paul W, Hadzic A, Joshi N, Alajaji F, Burlina P. TARA: Training and Representation Alteration for AI Fairness and Domain Generalization. Neural Comput 2022; 34:716-753. [PMID: 35016212 DOI: 10.1162/neco_a_01468] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2021] [Accepted: 08/08/2021] [Indexed: 11/04/2022]
Abstract
We propose a novel method for enforcing AI fairness with respect to protected or sensitive factors. This method uses a dual strategy performing training and representation alteration (TARA) for the mitigation of prominent causes of AI bias. It includes the use of representation learning alteration via adversarial independence to suppress the bias-inducing dependence of the data representation from protected factors and training set alteration via intelligent augmentation to address bias-causing data imbalance by using generative models that allow the fine control of sensitive factors related to underrepresented populations via domain adaptation and latent space manipulation. When testing our methods on image analytics, experiments demonstrate that TARA significantly or fully debiases baseline models while outperforming competing debiasing methods that have the same amount of information-for example, with (% overall accuracy, % accuracy gap) = (78.8, 0.5) versus the baseline method's score of (71.8, 10.5) for Eye-PACS, and (73.7, 11.8) versus (69.1, 21.7) for CelebA. Furthermore, recognizing certain limitations in current metrics used for assessing debiasing performance, we propose novel conjunctive debiasing metrics. Our experiments also demonstrate the ability of these novel metrics in assessing the Pareto efficiency of the proposed methods.
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Affiliation(s)
- William Paul
- Johns Hopkins University Applied Physics Laboratory Laurel, MD 20723, U.S.A.
| | - Armin Hadzic
- Johns Hopkins University Applied Physics Laboratory Laurel, MD 20723, U.S.A.
| | - Neil Joshi
- Johns Hopkins University Applied Physics Laboratory Laurel, MD 20723, U.S.A.
| | - Fady Alajaji
- Department of Mathematics and Statistics, Queens University, ON K7L 3N6, Canada
| | - Philippe Burlina
- Johns Hopkins University Applied Physics Laboratory Laurel, MD 20723, U.S.A., and Department of Computer Science, Johns Hopkins University, Baltimore, MD 21218, U.S.A.
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Dong L, Yang Q, Zhang RH, Wei WB. Artificial intelligence for the detection of age-related macular degeneration in color fundus photographs: A systematic review and meta-analysis. EClinicalMedicine 2021; 35:100875. [PMID: 34027334 PMCID: PMC8129891 DOI: 10.1016/j.eclinm.2021.100875] [Citation(s) in RCA: 25] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/22/2021] [Revised: 04/14/2021] [Accepted: 04/15/2021] [Indexed: 02/06/2023] Open
Abstract
BACKGROUND Age-related macular degeneration (AMD) is one of the leading causes of vision loss in the elderly population. The application of artificial intelligence (AI) provides convenience for the diagnosis of AMD. This systematic review and meta-analysis aimed to quantify the performance of AI in detecting AMD in fundus photographs. METHODS We searched PubMed, Embase, Web of Science and the Cochrane Library before December 31st, 2020 for studies reporting the application of AI in detecting AMD in color fundus photographs. Then, we pooled the data for analysis. PROSPERO registration number: CRD42020197532. FINDINGS 19 studies were finally selected for systematic review and 13 of them were included in the quantitative synthesis. All studies adopted human graders as reference standard. The pooled area under the receiver operating characteristic curve (AUROC) was 0.983 (95% confidence interval (CI):0.979-0.987). The pooled sensitivity, specificity, and diagnostic odds ratio (DOR) were 0.88 (95% CI:0.88-0.88), 0.90 (95% CI:0.90-0.91), and 275.27 (95% CI:158.43-478.27), respectively. Threshold analysis was performed and a potential threshold effect was detected among the studies (Spearman correlation coefficient: -0.600, P = 0.030), which was the main cause for the heterogeneity. For studies applying convolutional neural networks in the Age-Related Eye Disease Study database, the pooled AUROC, sensitivity, specificity, and DOR were 0.983 (95% CI:0.978-0.988), 0.88 (95% CI:0.88-0.88), 0.91 (95% CI:0.91-0.91), and 273.14 (95% CI:130.79-570.43), respectively. INTERPRETATION Our data indicated that AI was able to detect AMD in color fundus photographs. The application of AI-based automatic tools is beneficial for the diagnosis of AMD. FUNDING Capital Health Research and Development of Special (2020-1-2052).
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González‐Gonzalo C, Sánchez‐Gutiérrez V, Hernández‐Martínez P, Contreras I, Lechanteur YT, Domanian A, van Ginneken B, Sánchez CI. Evaluation of a deep learning system for the joint automated detection of diabetic retinopathy and age-related macular degeneration. Acta Ophthalmol 2020; 98:368-377. [PMID: 31773912 PMCID: PMC7318689 DOI: 10.1111/aos.14306] [Citation(s) in RCA: 39] [Impact Index Per Article: 9.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2019] [Revised: 07/29/2019] [Accepted: 10/31/2019] [Indexed: 01/14/2023]
Abstract
PURPOSE To validate the performance of a commercially available, CE-certified deep learning (DL) system, RetCAD v.1.3.0 (Thirona, Nijmegen, The Netherlands), for the joint automatic detection of diabetic retinopathy (DR) and age-related macular degeneration (AMD) in colour fundus (CF) images on a dataset with mixed presence of eye diseases. METHODS Evaluation of joint detection of referable DR and AMD was performed on a DR-AMD dataset with 600 images acquired during routine clinical practice, containing referable and non-referable cases of both diseases. Each image was graded for DR and AMD by an experienced ophthalmologist to establish the reference standard (RS), and by four independent observers for comparison with human performance. Validation was furtherly assessed on Messidor (1200 images) for individual identification of referable DR, and the Age-Related Eye Disease Study (AREDS) dataset (133 821 images) for referable AMD, against the corresponding RS. RESULTS Regarding joint validation on the DR-AMD dataset, the system achieved an area under the ROC curve (AUC) of 95.1% for detection of referable DR (SE = 90.1%, SP = 90.6%). For referable AMD, the AUC was 94.9% (SE = 91.8%, SP = 87.5%). Average human performance for DR was SE = 61.5% and SP = 97.8%; for AMD, SE = 76.5% and SP = 96.1%. Regarding detection of referable DR in Messidor, AUC was 97.5% (SE = 92.0%, SP = 92.1%); for referable AMD in AREDS, AUC was 92.7% (SE = 85.8%, SP = 86.0%). CONCLUSION The validated system performs comparably to human experts at simultaneous detection of DR and AMD. This shows that DL systems can facilitate access to joint screening of eye diseases and become a quick and reliable support for ophthalmological experts.
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Affiliation(s)
- Cristina González‐Gonzalo
- A‐eye Research GroupRadboud University Medical CenterNijmegenThe Netherlands,Diagnostic Image Analysis GroupRadboud University Medical CenterNijmegenThe Netherlands,Donders Institute for BrainCognition and BehaviourRadboud University Medical CenterNijmegenThe Netherlands,Department of OphthalmologyRadboud University Medical CenterNijmegenThe Netherlands
| | - Verónica Sánchez‐Gutiérrez
- Department of OphthalmologyUniversity Hospital Ramón y CajalRamón y Cajal Health Research Institute (IRYCIS)MadridSpain
| | - Paula Hernández‐Martínez
- Department of OphthalmologyUniversity Hospital Ramón y CajalRamón y Cajal Health Research Institute (IRYCIS)MadridSpain
| | - Inés Contreras
- Department of OphthalmologyUniversity Hospital Ramón y CajalRamón y Cajal Health Research Institute (IRYCIS)MadridSpain,Clínica RementeríaMadridSpain
| | - Yara T. Lechanteur
- Department of OphthalmologyRadboud University Medical CenterNijmegenThe Netherlands
| | - Artin Domanian
- Department of OphthalmologyRadboud University Medical CenterNijmegenThe Netherlands
| | - Bram van Ginneken
- Diagnostic Image Analysis GroupRadboud University Medical CenterNijmegenThe Netherlands
| | - Clara I. Sánchez
- A‐eye Research GroupRadboud University Medical CenterNijmegenThe Netherlands,Diagnostic Image Analysis GroupRadboud University Medical CenterNijmegenThe Netherlands,Donders Institute for BrainCognition and BehaviourRadboud University Medical CenterNijmegenThe Netherlands,Department of OphthalmologyRadboud University Medical CenterNijmegenThe Netherlands
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5
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Burlina PM, Joshi N, Pacheco KD, Liu TYA, Bressler NM. Assessment of Deep Generative Models for High-Resolution Synthetic Retinal Image Generation of Age-Related Macular Degeneration. JAMA Ophthalmol 2019; 137:258-264. [PMID: 30629091 DOI: 10.1001/jamaophthalmol.2018.6156] [Citation(s) in RCA: 75] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
Importance Deep learning (DL) used for discriminative tasks in ophthalmology, such as diagnosing diabetic retinopathy or age-related macular degeneration (AMD), requires large image data sets graded by human experts to train deep convolutional neural networks (DCNNs). In contrast, generative DL techniques could synthesize large new data sets of artificial retina images with different stages of AMD. Such images could enhance existing data sets of common and rare ophthalmic diseases without concern for personally identifying information to assist medical education of students, residents, and retinal specialists, as well as for training new DL diagnostic models for which extensive data sets from large clinical trials of expertly graded images may not exist. Objective To develop DL techniques for synthesizing high-resolution realistic fundus images serving as proxy data sets for use by retinal specialists and DL machines. Design, Setting, and Participants Generative adversarial networks were trained on 133 821 color fundus images from 4613 study participants from the Age-Related Eye Disease Study (AREDS), generating synthetic fundus images with and without AMD. We compared retinal specialists' ability to diagnose AMD on both real and synthetic images, asking them to assess image gradability and testing their ability to discern real from synthetic images. The performance of AMD diagnostic DCNNs (referable vs not referable AMD) trained on either all-real vs all-synthetic data sets was compared. Main Outcomes and Measures Accuracy of 2 retinal specialists (T.Y.A.L. and K.D.P.) for diagnosing and distinguishing AMD on real vs synthetic images and diagnostic performance (area under the curve) of DL algorithms trained on synthetic vs real images. Results The diagnostic accuracy of 2 retinal specialists on real vs synthetic images was similar. The accuracy of diagnosis as referable vs nonreferable AMD compared with certified human graders for retinal specialist 1 was 84.54% (error margin, 4.06%) on real images vs 84.12% (error margin, 4.16%) on synthetic images and for retinal specialist 2 was 89.47% (error margin, 3.45%) on real images vs 89.19% (error margin, 3.54%) on synthetic images. Retinal specialists could not distinguish real from synthetic images, with an accuracy of 59.50% (error margin, 3.93%) for retinal specialist 1 and 53.67% (error margin, 3.99%) for retinal specialist 2. The DCNNs trained on real data showed an area under the curve of 0.9706 (error margin, 0.0029), and those trained on synthetic data showed an area under the curve of 0.9235 (error margin, 0.0045). Conclusions and Relevance Deep learning-synthesized images appeared to be realistic to retinal specialists, and DCNNs achieved diagnostic performance on synthetic data close to that for real images, suggesting that DL generative techniques hold promise for training humans and machines.
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Affiliation(s)
- Philippe M Burlina
- Johns Hopkins University Applied Physics Laboratory, Baltimore, Maryland.,Malone Center for Engineering in Healthcare, Baltimore, Maryland.,Retina Division, Wilmer Eye Institute, Johns Hopkins University School of Medicine, Baltimore, Maryland
| | - Neil Joshi
- Johns Hopkins University Applied Physics Laboratory, Baltimore, Maryland
| | - Katia D Pacheco
- Brasilian Center of Vision Eye Hospital, Brasilia, Distrito Federal, Brazil
| | - T Y Alvin Liu
- Retina Division, Wilmer Eye Institute, Johns Hopkins University School of Medicine, Baltimore, Maryland
| | - Neil M Bressler
- Retina Division, Wilmer Eye Institute, Johns Hopkins University School of Medicine, Baltimore, Maryland.,Editor
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6
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Burlina PM, Joshi N, Pacheco KD, Freund DE, Kong J, Bressler NM. Use of Deep Learning for Detailed Severity Characterization and Estimation of 5-Year Risk Among Patients With Age-Related Macular Degeneration. JAMA Ophthalmol 2019; 136:1359-1366. [PMID: 30242349 DOI: 10.1001/jamaophthalmol.2018.4118] [Citation(s) in RCA: 88] [Impact Index Per Article: 17.6] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/06/2023]
Abstract
Importance Although deep learning (DL) can identify the intermediate or advanced stages of age-related macular degeneration (AMD) as a binary yes or no, stratified gradings using the more granular Age-Related Eye Disease Study (AREDS) 9-step detailed severity scale for AMD provide more precise estimation of 5-year progression to advanced stages. The AREDS 9-step detailed scale's complexity and implementation solely with highly trained fundus photograph graders potentially hampered its clinical use, warranting development and use of an alternate AREDS simple scale, which although valuable, has less predictive ability. Objective To describe DL techniques for the AREDS 9-step detailed severity scale for AMD to estimate 5-year risk probability with reasonable accuracy. Design, Setting, and Participants This study used data collected from November 13, 1992, to November 30, 2005, from 4613 study participants of the AREDS data set to develop deep convolutional neural networks that were trained to provide detailed automated AMD grading on several AMD severity classification scales, using a multiclass classification setting. Two AMD severity classification problems using criteria based on 4-step (AMD-1, AMD-2, AMD-3, and AMD-4 from classifications developed for AREDS eligibility criteria) and 9-step (from AREDS detailed severity scale) AMD severity scales were investigated. The performance of these algorithms was compared with a contemporary human grader and against a criterion standard (fundus photograph reading center graders) used at the time of AREDS enrollment and follow-up. Three methods for estimating 5-year risk were developed, including one based on DL regression. Data were analyzed from December 1, 2017, through April 15, 2018. Main Outcomes and Measures Weighted κ scores and mean unsigned errors for estimating 5-year risk probability of progression to advanced AMD. Results This study used 67 401 color fundus images from the 4613 study participants. The weighted κ scores were 0.77 for the 4-step and 0.74 for the 9-step AMD severity scales. The overall mean estimation error for the 5-year risk ranged from 3.5% to 5.3%. Conclusions and Relevance These findings suggest that DL AMD grading has, for the 4-step classification evaluation, performance comparable with that of humans and achieves promising results for providing AMD detailed severity grading (9-step classification), which normally requires highly trained graders, and for estimating 5-year risk of progression to advanced AMD. Use of DL has the potential to assist physicians in longitudinal care for individualized, detailed risk assessment as well as clinical studies of disease progression during treatment or as public screening or monitoring worldwide.
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Affiliation(s)
- Philippe M Burlina
- Applied Physics Laboratory, The Johns Hopkins University, Baltimore, Maryland
| | - Neil Joshi
- Applied Physics Laboratory, The Johns Hopkins University, Baltimore, Maryland
| | | | - David E Freund
- Applied Physics Laboratory, The Johns Hopkins University, Baltimore, Maryland
| | - Jun Kong
- The Fourth Affiliated Hospital of China Medical University, Eye Hospital of China Medical University, Shenyang
| | - Neil M Bressler
- Retina Division, Wilmer Eye Institute, Johns Hopkins University School of Medicine, Baltimore, Maryland.,Editor
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Burlina P, Joshi N, Pacheco KD, Freund DE, Kong J, Bressler NM. Utility of Deep Learning Methods for Referability Classification of Age-Related Macular Degeneration. JAMA Ophthalmol 2019; 136:1305-1307. [PMID: 30193354 DOI: 10.1001/jamaophthalmol.2018.3799] [Citation(s) in RCA: 32] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022]
Affiliation(s)
- Phillippe Burlina
- Applied Physics Laboratory, Johns Hopkins University, Baltimore, Maryland.,Retina Division, Wilmer Eye Institute, Johns Hopkins University School of Medicine, Baltimore, Maryland.,Department of Computer Science, Malone Center for Engineering in Healthcare, Johns Hopkins University, Baltimore, Maryland
| | - Neil Joshi
- Applied Physics Laboratory, Johns Hopkins University, Baltimore, Maryland
| | - Katia D Pacheco
- Retina Division, Department of Ophthalmology, Brazilian Center of Vision Eye Hospital, Brasilia, Brazil
| | - David E Freund
- Applied Physics Laboratory, Johns Hopkins University, Baltimore, Maryland
| | - Jun Kong
- Department of Ophthalmology, The Fourth Affiliated Hospital of China Medical University, Eye Hospital of China Medical University, Shenyang, China
| | - Neil M Bressler
- Retina Division, Wilmer Eye Institute, Johns Hopkins University School of Medicine, Baltimore, Maryland.,Editor
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Pead E, Megaw R, Cameron J, Fleming A, Dhillon B, Trucco E, MacGillivray T. Automated detection of age-related macular degeneration in color fundus photography: a systematic review. Surv Ophthalmol 2019; 64:498-511. [PMID: 30772363 PMCID: PMC6598673 DOI: 10.1016/j.survophthal.2019.02.003] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2018] [Revised: 01/31/2019] [Accepted: 02/04/2019] [Indexed: 12/13/2022]
Abstract
The rising prevalence of age-related eye diseases, particularly age-related macular degeneration, places an ever-increasing burden on health care providers. As new treatments emerge, it is necessary to develop methods for reliably assessing patients' disease status and stratifying risk of progression. The presence of drusen in the retina represents a key early feature in which size, number, and morphology are thought to correlate significantly with the risk of progression to sight-threatening age-related macular degeneration. Manual labeling of drusen on color fundus photographs by a human is labor intensive and is where automatic computerized detection would appreciably aid patient care. We review and evaluate current artificial intelligence methods and developments for the automated detection of drusen in the context of age-related macular degeneration.
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Affiliation(s)
- Emma Pead
- VAMPIRE Project, Centre for Clinical Brain Sciences, The University of Edinburgh, Edinburgh, Scotland.
| | - Roly Megaw
- Princess Alexandra Eye Pavilion, Edinburgh, Scotland
| | - James Cameron
- MRC Human Genetics Unit, The University of Edinburgh, Edinburgh, Scotland
| | - Alan Fleming
- Optos plc, Queensferry House, Carnegie Campus, Dunfermline
| | | | - Emanuele Trucco
- VAMPIRE Project, Computing (School of Science and Engineering), University of Dundee, UK
| | - Thomas MacGillivray
- VAMPIRE Project, Centre for Clinical Brain Sciences, The University of Edinburgh, Edinburgh, Scotland
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Li Z, Keel S, Liu C, He M. Can Artificial Intelligence Make Screening Faster, More Accurate, and More Accessible? Asia Pac J Ophthalmol (Phila) 2018; 7:436-441. [PMID: 30556381 DOI: 10.22608/apo.2018438] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022] Open
Abstract
Diabetic retinopathy, glaucoma, and age-related macular degeneration are leading causes of vision loss and blindness worldwide. They tend to be asymptomatic in the early phase of disease and therefore require active screening programs to identify the patients requiring referral and treatment. Deep learning-based artificial intelligence technology has recently become a major topic in the field of ophthalmology. This paper aimed to provide a general view of the major findings on the application of deep learning for the classification of eye diseases from common imaging modalities. In the future, it is expected that these technologies will be applied in real-world screening programs to improve their efficiency and affordability.
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Affiliation(s)
- Zhixi Li
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou, China
| | - Stuart Keel
- Centre for Eye Research Australia, Ophthalmology, Department of Surgery, University of Melbourne, Melbourne, Australia
| | - Chi Liu
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou, China
| | - Mingguang He
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou, China
- Centre for Eye Research Australia, Ophthalmology, Department of Surgery, University of Melbourne, Melbourne, Australia
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Burlina PM, Joshi N, Pekala M, Pacheco KD, Freund DE, Bressler NM. Automated Grading of Age-Related Macular Degeneration From Color Fundus Images Using Deep Convolutional Neural Networks. JAMA Ophthalmol 2017; 135:1170-1176. [PMID: 28973096 DOI: 10.1001/jamaophthalmol.2017.3782] [Citation(s) in RCA: 323] [Impact Index Per Article: 46.1] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/16/2023]
Abstract
Importance Age-related macular degeneration (AMD) affects millions of people throughout the world. The intermediate stage may go undetected, as it typically is asymptomatic. However, the preferred practice patterns for AMD recommend identifying individuals with this stage of the disease to educate how to monitor for the early detection of the choroidal neovascular stage before substantial vision loss has occurred and to consider dietary supplements that might reduce the risk of the disease progressing from the intermediate to the advanced stage. Identification, though, can be time-intensive and requires expertly trained individuals. Objective To develop methods for automatically detecting AMD from fundus images using a novel application of deep learning methods to the automated assessment of these images and to leverage artificial intelligence advances. Design, Setting, and Participants Deep convolutional neural networks that are explicitly trained for performing automated AMD grading were compared with an alternate deep learning method that used transfer learning and universal features and with a trained clinical grader. Age-related macular degeneration automated detection was applied to a 2-class classification problem in which the task was to distinguish the disease-free/early stages from the referable intermediate/advanced stages. Using several experiments that entailed different data partitioning, the performance of the machine algorithms and human graders in evaluating over 130 000 images that were deidentified with respect to age, sex, and race/ethnicity from 4613 patients against a gold standard included in the National Institutes of Health Age-related Eye Disease Study data set was evaluated. Main Outcomes and Measures Accuracy, receiver operating characteristics and area under the curve, and kappa score. Results The deep convolutional neural network method yielded accuracy (SD) that ranged between 88.4% (0.5%) and 91.6% (0.1%), the area under the receiver operating characteristic curve was between 0.94 and 0.96, and kappa coefficient (SD) between 0.764 (0.010) and 0.829 (0.003), which indicated a substantial agreement with the gold standard Age-related Eye Disease Study data set. Conclusions and Relevance Applying a deep learning-based automated assessment of AMD from fundus images can produce results that are similar to human performance levels. This study demonstrates that automated algorithms could play a role that is independent of expert human graders in the current management of AMD and could address the costs of screening or monitoring, access to health care, and the assessment of novel treatments that address the development or progression of AMD.
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Affiliation(s)
- Philippe M Burlina
- The Johns Hopkins University Applied Physics Laboratory, Laurel, Maryland
| | - Neil Joshi
- The Johns Hopkins University Applied Physics Laboratory, Laurel, Maryland
| | - Michael Pekala
- The Johns Hopkins University Applied Physics Laboratory, Laurel, Maryland
| | - Katia D Pacheco
- Retina Division, Brazilian Center of Vision Eye Hospital, Basilia, DF, Brazil
| | - David E Freund
- The Johns Hopkins University Applied Physics Laboratory, Laurel, Maryland
| | - Neil M Bressler
- Retina Division, Wilmer Eye Institute, Johns Hopkins University School of Medicine, Baltimore, Maryland.,Editor
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Burlina P, Billings S, Joshi N, Albayda J. Automated diagnosis of myositis from muscle ultrasound: Exploring the use of machine learning and deep learning methods. PLoS One 2017; 12:e0184059. [PMID: 28854220 PMCID: PMC5576677 DOI: 10.1371/journal.pone.0184059] [Citation(s) in RCA: 67] [Impact Index Per Article: 9.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2017] [Accepted: 08/17/2017] [Indexed: 12/12/2022] Open
Abstract
OBJECTIVE To evaluate the use of ultrasound coupled with machine learning (ML) and deep learning (DL) techniques for automated or semi-automated classification of myositis. METHODS Eighty subjects comprised of 19 with inclusion body myositis (IBM), 14 with polymyositis (PM), 14 with dermatomyositis (DM), and 33 normal (N) subjects were included in this study, where 3214 muscle ultrasound images of 7 muscles (observed bilaterally) were acquired. We considered three problems of classification including (A) normal vs. affected (DM, PM, IBM); (B) normal vs. IBM patients; and (C) IBM vs. other types of myositis (DM or PM). We studied the use of an automated DL method using deep convolutional neural networks (DL-DCNNs) for diagnostic classification and compared it with a semi-automated conventional ML method based on random forests (ML-RF) and "engineered" features. We used the known clinical diagnosis as the gold standard for evaluating performance of muscle classification. RESULTS The performance of the DL-DCNN method resulted in accuracies ± standard deviation of 76.2% ± 3.1% for problem (A), 86.6% ± 2.4% for (B) and 74.8% ± 3.9% for (C), while the ML-RF method led to accuracies of 72.3% ± 3.3% for problem (A), 84.3% ± 2.3% for (B) and 68.9% ± 2.5% for (C). CONCLUSIONS This study demonstrates the application of machine learning methods for automatically or semi-automatically classifying inflammatory muscle disease using muscle ultrasound. Compared to the conventional random forest machine learning method used here, which has the drawback of requiring manual delineation of muscle/fat boundaries, DCNN-based classification by and large improved the accuracies in all classification problems while providing a fully automated approach to classification.
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Affiliation(s)
- Philippe Burlina
- Applied Physics Laboratory, Johns Hopkins University, Laurel, Maryland, United States of America
| | - Seth Billings
- Applied Physics Laboratory, Johns Hopkins University, Laurel, Maryland, United States of America
| | - Neil Joshi
- Applied Physics Laboratory, Johns Hopkins University, Laurel, Maryland, United States of America
| | - Jemima Albayda
- Division of Rheumatology, Johns Hopkins School of Medicine, Baltimore, Maryland, United States of America
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Comparing humans and deep learning performance for grading AMD: A study in using universal deep features and transfer learning for automated AMD analysis. Comput Biol Med 2017; 82:80-86. [PMID: 28167406 DOI: 10.1016/j.compbiomed.2017.01.018] [Citation(s) in RCA: 105] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2016] [Revised: 01/18/2017] [Accepted: 01/26/2017] [Indexed: 12/20/2022]
Abstract
BACKGROUND When left untreated, age-related macular degeneration (AMD) is the leading cause of vision loss in people over fifty in the US. Currently it is estimated that about eight million US individuals have the intermediate stage of AMD that is often asymptomatic with regard to visual deficit. These individuals are at high risk for progressing to the advanced stage where the often treatable choroidal neovascular form of AMD can occur. Careful monitoring to detect the onset and prompt treatment of the neovascular form as well as dietary supplementation can reduce the risk of vision loss from AMD, therefore, preferred practice patterns recommend identifying individuals with the intermediate stage in a timely manner. METHODS Past automated retinal image analysis (ARIA) methods applied on fundus imagery have relied on engineered and hand-designed visual features. We instead detail the novel application of a machine learning approach using deep learning for the problem of ARIA and AMD analysis. We use transfer learning and universal features derived from deep convolutional neural networks (DCNN). We address clinically relevant 4-class, 3-class, and 2-class AMD severity classification problems. RESULTS Using 5664 color fundus images from the NIH AREDS dataset and DCNN universal features, we obtain values for accuracy for the (4-, 3-, 2-) class classification problem of (79.4%, 81.5%, 93.4%) for machine vs. (75.8%, 85.0%, 95.2%) for physician grading. DISCUSSION This study demonstrates the efficacy of machine grading based on deep universal features/transfer learning when applied to ARIA and is a promising step in providing a pre-screener to identify individuals with intermediate AMD and also as a tool that can facilitate identifying such individuals for clinical studies aimed at developing improved therapies. It also demonstrates comparable performance between computer and physician grading.
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Acharya UR, Mookiah MRK, Koh JEW, Tan JH, Noronha K, Bhandary SV, Rao AK, Hagiwara Y, Chua CK, Laude A. Novel risk index for the identification of age-related macular degeneration using radon transform and DWT features. Comput Biol Med 2016; 73:131-40. [PMID: 27107676 DOI: 10.1016/j.compbiomed.2016.04.009] [Citation(s) in RCA: 45] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2016] [Revised: 04/10/2016] [Accepted: 04/14/2016] [Indexed: 11/18/2022]
Abstract
Age-related Macular Degeneration (AMD) affects the central vision of aged people. It can be diagnosed due to the presence of drusen, Geographic Atrophy (GA) and Choroidal Neovascularization (CNV) in the fundus images. It is labor intensive and time-consuming for the ophthalmologists to screen these images. An automated digital fundus photography based screening system can overcome these drawbacks. Such a safe, non-contact and cost-effective platform can be used as a screening system for dry AMD. In this paper, we are proposing a novel algorithm using Radon Transform (RT), Discrete Wavelet Transform (DWT) coupled with Locality Sensitive Discriminant Analysis (LSDA) for automated diagnosis of AMD. First the image is subjected to RT followed by DWT. The extracted features are subjected to dimension reduction using LSDA and ranked using t-test. The performance of various supervised classifiers namely Decision Tree (DT), Support Vector Machine (SVM), Probabilistic Neural Network (PNN) and k-Nearest Neighbor (k-NN) are compared to automatically discriminate to normal and AMD classes using ranked LSDA components. The proposed approach is evaluated using private and public datasets such as ARIA and STARE. The highest classification accuracy of 99.49%, 96.89% and 100% are reported for private, ARIA and STARE datasets. Also, AMD index is devised using two LSDA components to distinguish two classes accurately. Hence, this proposed system can be extended for mass AMD screening.
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Affiliation(s)
- U Rajendra Acharya
- Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, 599489, Singapore; Department of Biomedical Engineering, School of Science and Technology, SIM University, 599491, Singapore; Department of Biomedical Engineering, Faculty of Engineering, University of Malaya, 50603, Malaysia
| | | | - Joel E W Koh
- Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, 599489, Singapore
| | - Jen Hong Tan
- Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, 599489, Singapore
| | - Kevin Noronha
- Department of Electronics and Telecommunication, St. Francis Institute of Technology, Mumbai 400103, India
| | - Sulatha V Bhandary
- Department of Ophthalmology, Kasturba Medical College, Manipal 576104, India
| | - A Krishna Rao
- Department of Ophthalmology, Kasturba Medical College, Manipal 576104, India
| | - Yuki Hagiwara
- Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, 599489, Singapore
| | - Chua Kuang Chua
- Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, 599489, Singapore
| | - Augustinus Laude
- National Healthcare Group Eye Institute, Tan Tock Seng Hospital, 308433, Singapore
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14
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Feeny AK, Tadarati M, Freund DE, Bressler NM, Burlina P. Automated segmentation of geographic atrophy of the retinal epithelium via random forests in AREDS color fundus images. Comput Biol Med 2015; 65:124-36. [PMID: 26318113 DOI: 10.1016/j.compbiomed.2015.06.018] [Citation(s) in RCA: 35] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2015] [Revised: 06/19/2015] [Accepted: 06/20/2015] [Indexed: 10/23/2022]
Abstract
BACKGROUND Age-related macular degeneration (AMD), left untreated, is the leading cause of vision loss in people older than 55. Severe central vision loss occurs in the advanced stage of the disease, characterized by either the in growth of choroidal neovascularization (CNV), termed the "wet" form, or by geographic atrophy (GA) of the retinal pigment epithelium (RPE) involving the center of the macula, termed the "dry" form. Tracking the change in GA area over time is important since it allows for the characterization of the effectiveness of GA treatments. Tracking GA evolution can be achieved by physicians performing manual delineation of GA area on retinal fundus images. However, manual GA delineation is time-consuming and subject to inter-and intra-observer variability. METHODS We have developed a fully automated GA segmentation algorithm in color fundus images that uses a supervised machine learning approach employing a random forest classifier. This algorithm is developed and tested using a dataset of images from the NIH-sponsored Age Related Eye Disease Study (AREDS). GA segmentation output was compared against a manual delineation by a retina specialist. RESULTS Using 143 color fundus images from 55 different patient eyes, our algorithm achieved PPV of 0.82±0.19, and NPV of 0:95±0.07. DISCUSSION This is the first study, to our knowledge, applying machine learning methods to GA segmentation on color fundus images and using AREDS imagery for testing. These preliminary results show promising evidence that machine learning methods may have utility in automated characterization of GA from color fundus images.
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Affiliation(s)
- Albert K Feeny
- Applied Physics Laboratory, The Johns Hopkins University, MD, USA; Department of Biomedical Engineering, The Johns Hopkins University, MD, USA
| | - Mongkol Tadarati
- Retina Division, Wilmer Eye Institute, The Johns Hopkins University, MD, USA; Rajavithi Hospital, College of Medicine, Rangsit University, Bangkok, Thailand
| | - David E Freund
- Applied Physics Laboratory, The Johns Hopkins University, MD, USA
| | - Neil M Bressler
- Retina Division, Wilmer Eye Institute, The Johns Hopkins University, MD, USA
| | - Philippe Burlina
- Applied Physics Laboratory, The Johns Hopkins University, MD, USA; Retina Division, Wilmer Eye Institute, The Johns Hopkins University, MD, USA; Department of Computer Science, The Johns Hopkins University, MD, USA
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15
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Local configuration pattern features for age-related macular degeneration characterization and classification. Comput Biol Med 2015; 63:208-18. [PMID: 26093788 DOI: 10.1016/j.compbiomed.2015.05.019] [Citation(s) in RCA: 38] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2015] [Revised: 05/25/2015] [Accepted: 05/26/2015] [Indexed: 12/30/2022]
Abstract
Age-related Macular Degeneration (AMD) is an irreversible and chronic medical condition characterized by drusen, Choroidal Neovascularization (CNV) and Geographic Atrophy (GA). AMD is one of the major causes of visual loss among elderly people. It is caused by the degeneration of cells in the macula which is responsible for central vision. AMD can be dry or wet type, however dry AMD is most common. It is classified into early, intermediate and late AMD. The early detection and treatment may help one to stop the progression of the disease. Automated AMD diagnosis may reduce the screening time of the clinicians. In this work, we have introduced LCP to characterize normal and AMD classes using fundus images. Linear Configuration Coefficients (CC) and Pattern Occurrence (PO) features are extracted from fundus images. These extracted features are ranked using p-value of the t-test and fed to various supervised classifiers viz. Decision Tree (DT), Nearest Neighbour (k-NN), Naive Bayes (NB), Probabilistic Neural Network (PNN) and Support Vector Machine (SVM) to classify normal and AMD classes. The performance of the system is evaluated using both private (Kasturba Medical Hospital, Manipal, India) and public domain datasets viz. Automated Retinal Image Analysis (ARIA) and STructured Analysis of the Retina (STARE) using ten-fold cross validation. The proposed approach yielded best performance with a highest average accuracy of 97.78%, sensitivity of 98.00% and specificity of 97.50% for STARE dataset using 22 significant features. Hence, this system can be used as an aiding tool to the clinicians during mass eye screening programs to diagnose AMD.
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16
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Automated diagnosis of Age-related Macular Degeneration using greyscale features from digital fundus images. Comput Biol Med 2014; 53:55-64. [DOI: 10.1016/j.compbiomed.2014.07.015] [Citation(s) in RCA: 44] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2014] [Revised: 07/04/2014] [Accepted: 07/20/2014] [Indexed: 01/19/2023]
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